A relationship between rock physics and NMR (Nuclear ... · A relationship between rock physics and NMR attributes in the Ip-V p /V s space to describe the various rock properties
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A relationship between rock physics and NMR (Nuclear Magnetic Resonance) Zakir Hossain, Houston, USA
Summary
Historically nuclear magnetic resonance (NMR) is used as
a useful tool in petrophysical based reservoir evaluation.
The objective of this study is to define a relationship
between NMR T2 distribution and seismic attributes for
accurately rock properties prediction. To define rock
properties, we used laboratory NMR and ultrasonic
measured data, rock physics, and AVO analysis. From this
study, we define the following relationship:
iTR
2total
11
cutoffimicro TR ,2
cuttoffiendiPFM TTRV ,2,2
cuttoffiendi
gTTR
S,2,2
111
where, total is the total porosity, and are the Lame`
parameters related with P-impedance and S-impedance, T2i
is the NMR T2 distribution of each pore, VPFM is the
volume of pore-filling mineral (PFM), Sg is the gas
saturation, R is the or S-reflection coefficient, R
is the (reflection coefficient. This study shows
that NMR T2 distributions are directly related with seismic
attributes. Therefore, integrating NMR data with rock
physics analysis provides a solid basis for quantitative
seismic petrophysical interpretation by minimizing
interpretation risk.
Introduction
NMR is a useful tool to measure in-situ reservoir
properties. However, historically nuclear magnetic
resonance (NMR) is used for fundamental petrophysical
properties prediction including porosity, permeability,
irreducible water saturation, capillary pressure (Howard, et
al. 1993; Kenyon et al. 1995; Kenyon 1997; Hossain et al.
2011a). Recently, Hossain et al. 2011b and 2011c showed
that NMR can be used as a potential tool to understand the
fluid flow distribution and fluid related dispersion. They
described that Biot’s flow occurs only in large pores in
complex rocks while, Biot’s flow should not occur in
micro-pores. Differences of fluid flow in macro-pores and
micro-pores pores are described as the high frequency
squirt flow in complex rocks. Thus, NMR analysis helps us
to understand and quantify the different pores,
heterogeneous of pore types and their distribution, and
changing pore fluids. In contrast, rock physics analysis
helps us to understand and quantify the different
lithologies, changing pore fluids, heterogeneous of pore
types and their distribution, and elastic properties in
general. Therefore, integrating NMR data with rock physics
analysis provides a solid basis for quantitative seismic
petrophysical interpretation. The objective of this study is
to define a relationship between NMR measurement and
rock physics measurement for rock properties prediction.
Method
We used laboratory measured NMR and ultrasonic P-and
S-wave velocities measured data on brine saturated
greensand samples. All data used for this study were
published by Hossain (2011). Data representing the CO2
bearing state were calculated by using Gassmann’s
equations (Gassmann, 1951). The CO2 properties as a
function of temperature and pressure were derived based on
data from Wang et al. (2010), and brine properties were
calculated from equations of Batzle and Wang (1992)
In addition, rock physic and AVO modeling were done to
predict rock properties from ultrasonic measurement. To
predict rock properties from sonic data, we generated an
RPT (Hossain et al., 2015) which combined multiple
Figure 1: NMR measurement on fully saturated sample is
compared to the NMR measurement after centrifuging at 100 psi.
The cutoff time, which separates the T2 distribution into macro-
porosity and micro-porosity is defined as the relaxation time at the point where the cumulative porosity of the fully saturated sample
equals the irreducible water saturation. The dashed vertical line is
shown a cutoff of 5.21ms. High total porosity is a function of high
cumulative T2i low ,and low R; high micro-porosity is a
function of high cumulative T2,cutoff, high and high R,; high
volume of pore filling mineral (PFM) is function of high
high R and slow T2; high gas saturation is function of
low , low R, and fast T2. (Figure modified after Hossain
attributes in the Ip-Vp/Vs space to describe the various rock
properties from seismic data. For AVO analysis, we
generated an RPT in the intercept-gradient space. To
generate an RPT in the intercept-gradient space, constant
shear-reflection coefficient curves (Rs) were calculated
based on the following relationship (Wiggins et at. 1983):
SP RRG 2 (1)
where, RP is the P-reflection coefficient or intercept, RS is
the S-reflection coefficient which is equivalent to R, and
G is the gradient.
We used following relationships to calculate -reflection
coefficient and (reflection coefficient:
12
12
R (2)
)()(
)()(
1122
1122
R (3)
where, and (are related with lp and ls. and
(represent cap rock properties, whereas and
(represent reservoir rock properties.
Equations (1)-(3) were used to generate constant
reflection coefficient curves as well as constant
(-reflection coefficient curves in the intercept-
gradient space (Figure 4).
Initially, intercept and gradient were calculated for brine
saturated samples and shale interface. Then intercept
gradient were calculated for CO2 saturated samples and
shale interface. Intercept and gradient were calculated
based on Castagna and Smith (1994). The shale represents
the cap-rock for the greensand. Shale data for AVO curves
were obtained from the studied Nini 1A well (Hossain et al.
2012).
Results
The NMR T2 distributions are presented in graphical form
for each sample (Figure 1 and Figure 2). All greensand
have bimodal T2 distributions. Each T2 time corresponds to
a particular pore size. For the present greensand samples, a
peak close to 1 ms should correspond to glauconite water,
whereas all samples also present a second peak close to 100
ms that corresponds to movable fluid (Hossain et al.,
Figure 2: Geological properties defined from NMR measurements. (a) and (c) BSE images and conceptual models of two types of greensand
from the North Sea. Scale bar of these images is 200 m and the images represent macro-porosity, quartz and glauconite grains and micro-
porosity within glauconite. (a) Weakly cemented greensand (c) Micro crystalline quartz and pore-filling berthierine cemented greensand (Images
and rock model after Hossain et al., 2011). (b) NMR T2 distributions are presented in graphical form for weakly cemented and cemented samples. It is noticeable that weakly cemented samples show larger amplitude in the movable fluid than cemented samples; whereas highly diagenetically
altered samples show slightly larger amplitude in glauconite water (NMR data from Hossain, 2011).
EDITED REFERENCES Note: This reference list is a copyedited version of the reference list submitted by the author. Reference lists for the 2016
SEG Technical Program Expanded Abstracts have been copyedited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web.
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